Drone-based infrastructure inspection
Inspect power lines, pylons and industrial sites with computer vision, without tying up your teams.
The challenge
Inspecting energy, mining and construction infrastructure is still largely manual: crews sent on site, scaffolding, production shutdowns, technicians exposed to hazardous environments. Across vast networks — high-voltage lines, kilometres of conveyors, solar panels in the thousands — coverage is slow, partial and costly.
Drones now produce masses of high-resolution imagery, but visual analysis by experts is a bottleneck: thousands of shots per flight, fatigue that lets early defects slip through, and no quantitative tracking of how degradation evolves.
Our approach
Shift turns your drone imagery into automated diagnostics. We train object-detection models to locate components (insulators, joints, panels, welds) and segmentation models to precisely delineate defects: corrosion, cracks, hot spots, vegetation encroaching on lines, failing solar modules.
Defects are classified by type and severity, geolocated on each asset and prioritised for maintenance. By combining visible and thermal imaging, we detect anomalies invisible to the naked eye, such as the overheating that precedes failure.
Everything is delivered in a map-based dashboard that tracks degradation flight after flight, feeds your CMMS, and enables the shift from corrective to predictive maintenance, planned around the assets genuinely at risk.
Architecture
- Detection: YOLO / Faster R-CNN to locate components and anomalies
- Segmentation: U-Net / Mask R-CNN to delineate corrosion, cracks, hot spots
- Fusion: RGB + thermal imaging, defect georeferencing
- Delivery: map dashboard, prioritisation and CMMS integration
An energy operator cuts unplanned outages by 40% by moving to predictive maintenance based on automated analysis of drone imagery.